code: def check_data(x, y, data_config):
      from bigdl.nano.utils.common import invalidInputError
  
      invalidInputError(
          data_config["past_seq_len"] == x.shape[-2],
          "The x shape should be (batch_size, past_seq_len, input_feature_num), "
          "Got past_seq_len of {} in config while x input shape of {}.".format(
              data_config["past_seq_len"], x.shape[-2]
          ),
      )
      invalidInputError(
          data_config["future_seq_len"] == y.shape[-2],
          "The y shape should be (batch_size, future_seq_len, output_feature_num),"
          " Got future_seq_len of {} in config while y input shape of {}.".format(
              data_config["future_seq_len"], y.shape[-2]
          ),
      )
      invalidInputError(
          data_config["input_feature_num"] == x.shape[-1],
          "The x shape should be (batch_size, past_seq_len, input_feature_num),"
          " Got input_feature_num of {} in config while x input shape of {}.".format(
              data_config["input_feature_num"], x.shape[-1]
          ),
      )
      invalidInputError(
          data_config["output_feature_num"] == y.shape[-1],
          "The y shape should be (batch_size, future_seq_len, output_feature_num),"
          " Got output_feature_num of {} in config while y input shape of {}.".format(
              data_config["output_feature_num"], y.shape[-1]
          ),
      )
  
imports: import torch
  import torch.nn
  import warnings
  import random
  import numpy
  from torch.utils.data import TensorDataset, DataLoader
  import numpy
  import multiprocessing
  import warnings
  import math
label_desc: Use image node_id 7 for calling a global function with example usage: check_data(x, y, data_config) without return types